prepro.py 文件源码

def elastic_transform_multi(x, alpha, sigma, mode="constant", cval=0, is_random=False):
    """Elastic deformation of images as described in `[Simard2003] <http://deeplearning.cs.cmu.edu/pdfs/Simard.pdf>`_.

    Parameters
    -----------
    x : list of numpy array
    others : see ``elastic_transform``.
    """
    if is_random is False:
        random_state = np.random.RandomState(None)
    else:
        random_state = np.random.RandomState(int(time.time()))

    shape = x[0].shape
    if len(shape) == 3:
        shape = (shape[0], shape[1])
    new_shape = random_state.rand(*shape)

    results = []
    for data in x:
        is_3d = False
        if len(data.shape) == 3 and data.shape[-1] == 1:
            data = data[:,:,0]
            is_3d = True
        elif len(data.shape) == 3 and data.shape[-1] != 1:
            raise Exception("Only support greyscale image")
        assert len(data.shape)==2

        dx = gaussian_filter((new_shape * 2 - 1), sigma, mode=mode, cval=cval) * alpha
        dy = gaussian_filter((new_shape * 2 - 1), sigma, mode=mode, cval=cval) * alpha

        x_, y_ = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), indexing='ij')
        indices = np.reshape(x_ + dx, (-1, 1)), np.reshape(y_ + dy, (-1, 1))
        # print(data.shape)
        if is_3d:
            results.append( map_coordinates(data, indices, order=1).reshape((shape[0], shape[1], 1)))
        else:
            results.append( map_coordinates(data, indices, order=1).reshape(shape) )
    return np.asarray(results)

# zoom